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Shifting ranges and conservation challenges for lemurs in
the face of climate change
Jason L. Brown & Anne D. Yoder
Biology Department, Duke University, Durham, North Carolina 27705
Keywords
ANUSPLIN, BIOMOD, ecological niche
modeling, ensemble, least-cost corridors,
Madagascar, micro-endemism, pseudoabsence selection, species distribution
modeling, Strepsirrhini.
Correspondence
Jason L. Brown, Biology Department, Duke
University, Durham, NC 27705.
Tel: 919-660-7372; Fax: 919-660-7293;
E-mail: [email protected]
Present address
Jason L. Brown, Biology Department, The
City College of New York, New York, New
York, 10031
Funding information
JLB received salary support from Duke
University Start-up funds to ADY. This is
Duke Lemur Center publication: #1279.
Received: 7 November 2014; Revised: 14
December 2014; Accepted: 7 January 2015
Ecology and Evolution 2015; 5(6):
1131–1142
doi: 10.1002/ece3.1418
Abstract
Geospatial modeling is one of the most powerful tools available to conservation
biologists for estimating current species ranges of Earth’s biodiversity. Now,
with the advantage of predictive climate models, these methods can be
deployed for understanding future impacts on threatened biota. Here, we
employ predictive modeling under a conservative estimate of future climate
change to examine impacts on the future abundance and geographic distributions of Malagasy lemurs. Using distribution data from the primary literature,
we employed ensemble species distribution models and geospatial analyses to
predict future changes in species distributions. Current species distribution
models (SDMs) were created within the BIOMOD2 framework that capitalizes
on ten widely used modeling techniques. Future and current SDMs were then
subtracted from each other, and areas of contraction, expansion, and stability
were calculated. Model overprediction is a common issue associated Malagasy
taxa. Accordingly, we introduce novel methods for incorporating biological
data on dispersal potential to better inform the selection of pseudo-absence
points. This study predicts that 60% of the 57 species examined will experience
a considerable range of reductions in the next seventy years entirely due to
future climate change. Of these species, range sizes are predicted to decrease by
an average of 59.6%. Nine lemur species (16%) are predicted to expand their
ranges, and 13 species (22.8%) distribution sizes were predicted to be stable
through time. Species ranges will experience severe shifts, typically contractions,
and for the majority of lemur species, geographic distributions will be considerably altered. We identify three areas in dire need of protection, concluding
that strategically managed forest corridors must be a key component of lemur
and other biodiversity conservation strategies. This recommendation is all the
more urgent given that the results presented here do not take into account patterns of ongoing habitat destruction relating to human activities.
Introduction
Climate change is a looming threat to Earth’s biodiversity,
with island ecosystems being among the most gravely
threatened (Wetzel et al. 2012, 2013; Gibson et al. 2013).
Entire faunal assemblages may perish as a consequence
(Ricketts et al. 2005; Pounds et al. 2006; Moritz and Agudo 2013). Geospatial analyses can be utilized to forecast
the directionality and magnitude of shifting habitats and
thus be deployed for predicting the future distribution
of biodiversity. Conservation biologists and affected
governing authorities can then reference these predictions
to implement proactive strategies (Heller and Zavaleta
2009; Rakotomanana et al. 2013). Although the lemurs of
Madagascar have been identified as the world’s most
endangered vertebrates (Schwitzer et al. 2013), it is
unknown how climate change will impact their future
distributions and already high-risk status. For example,
lemur reproduction has been linked with climatic variability (Dunham et al. 2011), and climatic changes may
be an additional threat. To look at the potential distribution changes resulting from future climate change, we
employ ensemble species distribution models (Araujo and
New 2007) under a conservative model (IPCC 2007) to
ª 2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use,
distribution and reproduction in any medium, provided the original work is properly cited.
1131
Lemurs in the Face of Climate Change
J. L. Brown & A. D. Yoder
assess the impact of climate change on future abundance
and distributions of 57 lemur species. The predictive
power of these analyses is enhanced by novel methods
derived here for the incorporation of biological data to
better inform the selection of pseudo-absence points. The
results reveal a complex mixture of future habitat stability
and contraction. We identify three areas in dire need of
protection; each with different levels of current forestation
and governmental protection, and each with differing climatic and ecological profiles. Most notably, the results of
this study indicate that ranges will shift for the majority
of lemur species, in certain cases, by hundreds of kilometers. It is therefore imperative for the long-term survival
of lemurs that conservation strategies be implemented for
the establishment and protection of forest corridors.
These corridors will provide the means for lemur (and
other) species to follow suitable habitats as they shift in
response to a changing global climate (Moilanen et al.
2009).
In July 2012, the International Union for Conservation
of Nature (IUCN) species survival group concluded that
lemurs are the most endangered group of vertebrates on
Earth, with threat levels exceeding those of all other
mammals, amphibians, birds (including reptiles), and boney fishes (Schwitzer et al. 2013). Of the 103 species recognized by the IUCN’s Red List of Threatened Species,
94% are threatened (24 as critically endangered, 49 as
endangered, and 20 vulnerable)(Schwitzer et al. 2013).
The ever-worsening status is associated with numerous
human pressures including population growth and
extreme poverty, introduced invasive species, illegal logging, mining, subsistence farming, and increased pressures
from poaching and the bushmeat trade (Barrett and Ratsimbazafy 2009; Barrett et al. 2010; Rakotomanana et al.
2013). Approximately 80% of Madagascar’s population
lives in rural areas and depends on subsistence agriculture. In the last two centuries, deforestation has claimed
more than 90% of the island’s natural habitats (Ingram
and Dawson 2005; Harper et al. 2007). The remaining
unprotected forests are under unrelenting pressure from
the slash-and-burn cultivation of rice (Erdmann 2003) as
well as from the collection of lumber for cooking and
construction.
Annually, the Malagasy consume 22,000,000 m3 of
wood (Rabenandrasana 2007). Given that the current
population of 21.9 million human inhabitants is projected
to grow to 53.6 million by the year 2050 (Rakotomanana
et al. 2013), which will result in increased pressure on
Madagascar’s natural habitats, the need for targeted
conservation action is urgent requiring both short- and
long-term planning. These pressures place the survival of
lemurs and other members of Madagascar’s highly ende-
mic biota under grave threat. Climate change has been
recognized as one of the most important determinants for
changing species distributions (Araujo and Rahbek 2006;
Parmesan 2006; Beaumont et al. 2007), with regional
changes in precipitation and temperature being especially
potent forces (Peters and Darling 1985; Parmesan 1996;
Hannah et al. 2008; Busch et al. 2012). Several studies
have demonstrated that species are already experiencing
distribution shifts toward the poles or to higher elevations, with some species driven to extinction by rapid climate change (Parmesan and Yohe 2003; Root et al.
2003). Such global-scale studies have been expanding rapidly over the past several decades. More recently, there
has been a call for a focus on smaller and more rapid
assessments of “pressing questions that have a particular
political interest and for which science is evolving
quickly” (Editoral 2013). This call is especially relevant to
the conservation crisis currently facing Madagascar’s
highly threatened endemic biota. Conservation planning
in Madagascar has prioritized areas that include a network of habitats containing as many endemic species as
possible (Kremen et al. 2008). In August 2013, the IUCN
published a comprehensive lemur conservation action
plan that aimed to maximize the immediate survival of
lemurs by outlining management goals for the next
3 years (Schwitzer et al. 2013). These measures, however,
do not take into account the directionality, magnitude or
probability of species’ future range shifts in response to a
changing climate. Many populations can respond by shifting from altered and unsuitable habitats to more favorable adjacent habitats, although such range shifts are only
feasible if suitable intervening habitats persist and are
accessible.
Here, we aim to estimate the distributional changes of
lemurs in response to climate change. We identify and
characterize key areas of species richness and endemism
in present-day Madagascar, comparing present estimates
to those projected to occur 70 years from now (ca. year
2080). On the basis of these comparisons, we identify
areas of key conservation priority required to maximize
the persistence of lemur species, and by extension, the
myriad endemic biodiversity with which they share their
habitat.
1132
ª 2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
Methods
Species occurrence data
We compiled 4600 occurrence localities on all species of
lemurs from the primary literature (see Table S2 for details)
and from the national database, REBIOMA, a database with
expertly vetted occurrence data (http://data.rebioma.net).
J. L. Brown & A. D. Yoder
Only species with ≥6 unique occurrence points were modeled (Pearson et al. 2007; Kramer-Schadt et al. 2013); the
final data set represented 57 lemur species sampled
throughout Madagascar with 6–133 unique localities per
species. These occurrence data contained at least one species from every genus of extant lemur (see Table S2) and
were vetted by experts for accuracy. Spatial sampling biases
were corrected by rarefying the data, randomly selecting a
single occurrence point when many are present within a
shared area, at a 5 km2 spatial resolution for non-microendemic species (with minimum convex polygons
≥2500 km2)( Peterson et al. 2011; Kramer-Schadt et al.
2013).
Climate data
The current and future climate data were comprised of
the original 19 standard Bioclim variables [30 arc-sec resolution, from worldclim.org (Hijmans et al. 2005) and
ICP4.org (IPCC 2007)] and 16 additional Bioclim variables (variables 20–36, 2.5 arc-min resolution) pertaining
to soil moisture and solar radiation [from the Climond.org data set (Kriticos et al. 2012)]. The 16 variables
from Climond.org were only available at a coarser resolution (2.5 arc-min vs. 30 arc-sec) and were subsequently
downscaled to 30 arc-sec using the ANUSPLIN method, a
thin-plate smoothing method for noisy data (as follow by
Hijmans et al. 2005). In brief, a high-resolution digital
elevation model, latitude, and longitude were used as
independent variables to predict local spatial relationships
of the coarser climate data and used interpolate the climate data into higher spatial resolution (Hijmans et al.
2005). An additional independent variable, annual precipitation, was used for the downscaling of variables pertaining to solar radiation (Bioclim 20-27). The inclusion of
annual precipitation allowed for dependences of solar
radiation on cloud cover associated with rainfall (Hutchinson et al. 1984). Two additional independent variables,
slope and aspect (in additional to elevation), were used to
downscale the climate variables pertaining to soil moisture. These were included because both affect the amount
of solar radiation that habitats receive and thus influence
the soil moisture and water retention (Geroy et al. 2011).
These methods were repeated for the Bioclim variables
20–36 for the current climate data and two 2080 global
circulation models (all initially from Climong.org). The
final downscaled variables are available for download at
www.SDMtoolbox.org. All variables were assessed for cocorrelation using a Pearson r correlation. Of the 36 Bioclim variables, 16 variables were co-correlated below
0.75 r2 and were subsequently used for species distribution modeling [Bio1–4, 6, 12–14, 18, 20, 22–26, 31; performed in SDMtoolbox v1.0 (Brown 2014)].
ª 2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
Lemurs in the Face of Climate Change
Climate change projections were obtained from the
Intergovernmental Panel on Climate Change (IPCC 2007)
and Climond.org (Kriticos et al. 2012). The two global
circulation models used were CSIRO Mark 3.0 (CSIRO,
Australia) and MIROC-H (Center for Climate Research,
Japan). The two models covered the high and low of climate sensitivity at each emission scenario, reflecting the
amount of global warming for a doubling of the atmospheric CO2 concentration compared with 1990 levels
(CSIRO Mark 3.0: 2.11°C and MIROC-H: 4.13°C). We
used the A1B emission scenario as it represents a moderate temperature change scenario. In brief, this model projects a future world of rapid economic growth, new and
more efficient energy technologies, and convergence
between regions (IPCC 2007). The A1B scenario adopts a
balance across all energy sources (fossil and renewable)
for the technological change in the energy system (IPCC
2007). This scenario has been extensively used (van der
Linden and Mitchell 2009) and represents a medium
emission trajectory and results in midrange estimates of
average global changes (IPCC 2007). Lastly, the Bioclim
variables 20–36 were available for both current and future
scenarios (Kriticos et al. 2012), thus making them directly
comparable.
Species distribution models
Current species distribution models (SDMs) were created
within the BIOMOD2 framework (Thuiller et al. 2009) (R
package Biomod2). BIOMOD2 is a SDM method that
capitalizes on ten widely used modeling techniques: artificial neural networks (ANN)(Ripley 1996), classification
tree analysis (CTA)(Breiman 1984), generalized additive
models (GAM)(Hastie et al. 1994), generalized linear
models (GLM)(McCullagh and Nelder 1989), generalized
boosted models (GBM, also known as boosted regression
trees)(Ridgeway 1999), flexible discriminant analysis
(FDA)(Hastie et al. 1994), multivariate adaptive regression
splines (MARS)(Friedman 1991), Breiman and Cutler’s
random forest for classification and regression (RF)(Breiman 2001), surface range envelope (SRE, a.k.a. BIOCLIM)
(Busby 1991), and maximum entropy (MaxEnt)(Phillips
et al. 2006). BIOMOD2 accounts for intermodel variability
by fitting ensembles of forecasts by simulating across more
than one set of initial conditions, model classes, model
parameters, and boundary conditions (see Araujo and
New 2007 for a review). BIOMOD2 analyzes the resulting
range of uncertainties with regard to area bounded by
models, predictive consensus, and probabilistic density
functions summarizing the likelihood of a species presence
estimated from a large ensemble of SDMs (Araujo and
New 2007; Thuiller 2007). In lemur species with a number
of unique occurrences points >10, model calibration was
1133
Lemurs in the Face of Climate Change
performed on a random sample of the data (75%), and
model evaluation was carried out on the remaining 25%
with the true skill statistic (TSS) (10 replicates of each species). For species with 6–9 occurrences (n = 9), localities
were jackknifed to evaluate and build the model. After the
individual models were built, they were ensembled and
converted to binary models based on a TSS cutoff of 0.85.
Each ensemble model was proofed by a taxonomic expert.
Based on the optimal of number pseudo-absences (PAs)
required, the 10 modeling techniques were placed in three
classes (high PAs, mid PAs, low PAs) and ensemble modeling was performed for each class (see below for information on the number PAs for each class)(Barbet-Massin
et al. 2012). Each PA class produced a single, continuous
probabilistic density ensemble model that was then
weighted by the number of models included. The three
weighted layers were summed to produce the final continuous ensemble model for each climate scenario. For each
PA class final, binary ensemble models were summed and
the product was divided by the number of classes contributing to each model to produce the final model. For the
two SDMs based on the future global circulation models,
ensemble models were averaged prior to conversion to a
binary model. The final ensemble SDM was converted to a
binary model by classifying values greater than (or equal
to) 0.5 to a value of 1 and those lower to a value of 0. All
final binary models (both current and future) were then
clipped by areas of natural vegetation in 2005 (ONE
2009). This model represented the predicted binary distribution of each species (see Figure 1 for an overview of
methods).
To minimize future predictions in habitats that could
not be feasibly dispersed into by the year 2080, a maximum dispersal limit of 100 km (ca. 1.4 km/year) was
enforced from areas of “suitable habitat” in contemporary
distributions (Anderson 2013; performed in SDMtoolbox
v1.0, Brown 2014). Future nonanalogous climates (FNAC)
were also identified, and inferences from models projected
into these areas were interpreted with caution (see Fig.
S3), particularly if ranges were predicted to expand to
these environments (vs. predictions of stability or
decline). Due to uncertainty of species’ responses under
such scenarios, no conservation recommendations were
based on species’ expansions or contractions into geographic areas with FNAC. However, predictions of stability in FNAC helped to determine the second priority area
(habitats surrounding the Mangoky River). Not surprisingly (due to persisting suitability), the nonanalogous
variables in this area (identified by running separate
MESS plots in MaxEnt) were not central to predicting
habitat suitability for these species.
1134
J. L. Brown & A. D. Yoder
Pseudo-absence selection
Model overprediction is a common issue associated with
models derived for Malagasy taxa. This is, in large part,
due to the physiognomy of Madagascar that extends latitudinally (12–16°S) with three mountain massifs extending down the spine. The country is much narrower
longitudinally (situated diagonally across 43–51°E) and a
rain-shadow effect produces an arid western versant
(Goodman and Benstead 2003). These factors create steep
environmental gradients longitudinally, and many species’
distributions extend latitudinally across suitable ecotones.
This type of physiognomy, coupled with areas of considerable topographic heterogeneity, is especially problematic
for model evaluation and for selection of appropriate
pseudo-absences.
Pseudo-absences (PAs) are meant to be compared with
the presence data and help differentiate the environmental
conditions under which a species can potentially occur.
Typically, PAs are selected within a large rectilinear area,
within this area there often exists habitat that is environmentally suitable, but was never colonized. When background points are selected within these habitats, this
increases commission errors (false positives). As a result,
the “best” performing model tends to be overfit because
selection criterion favor a model that fails to predict the
species in the climatically suitable, uncolonized habitats
(Anderson & Raza, 2010; Barbet-Massin et al. 2012). The
likelihood that suitable unoccupied habitats are included
in background sampling increases with Euclidian distance
from the species’ realized range. Thus, a larger study spatial extent can lead to the selection of a higher proportion
of less informative background points (Barbet-Massin
et al. 2012).
To circumvent this problem, many researchers have
begun using PA selection methods that are more regional
(VanDerWal et al. 2009). One common method constitutes sampling PAs within a maximum radial distance of
known occurrences (Thuiller et al. 2009). However, selection of the maximum distance parameter can be difficult
to choose and should reflect a realistic dispersal distance
(e.g., habitat that is potential reachable through evolutionary time). Here, we selected the maximum radial
search distance (MRSD) by calculating the area of a minimum convex polygon (or minimum convex polygon area,
MCPA) from occurrence points and transforming them
to reflect a logistic curve with MRSD values from 50 to
300 km. This method is more sensitive to changes in
smaller range sizes. For example, it will apply a MRSD of
50, 100, and 200 km to species with MCPA of 100, 4000,
and 40,000 km2, respectively (see equation in Box 1).
ª 2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
J. L. Brown & A. D. Yoder
Box 1. Pseudo-absence maximum radial search distance equation
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
a ¼ MCP area b ¼ log10 a
k ¼ min search distance ðhere 30 kmÞ
a
þk
PAmaxDis ¼ 2
b
If PAmaxDis [ 300 km then PAmaxDis ¼ 300 km
Based on the optimal number of pseudo-absences (PAs)
required, the 10 modeling techniques were placed in three
classes: high PAs (GAM, GLM, SRE, MaxEnt, and ANN),
mid PAs (MARs and FDA), and low PAs (CTA, GBM,
RF). For the high PAs class, 10,000 PAs were used for all
models (Barbet-Massin et al. 2012). In the mid and low
PAs classes, the number of PAs equaled the number of
unique occurrence points multiplied by 10 and 4, respectively (average, min–max: 324, 60–1330, and 130, 24–532)
(Barbet-Massin et al. 2012).
Geospatial analyses
To measure the predicted distribution changes for each
species, the binary SDMs were projected to Africa Albers
Equal-Area Cylindrical projection in ArcMap 10.1 (ESRI
2012) at a spatial resolution of 0.9109 km2. The final binary models were then clipped by areas of natural vegetation in 2005 (ONE 2009) that limited species’ current
distributions to areas of natural vegetation.
The future and current SDMs were then subtracted
from each other, and areas of contraction, expansion, and
stability were calculated. Each of these individual classes
was summed separately for all species, generating a map
displaying the intensity of contraction, expansion, and
stability throughout Madagascar. Expansion was classified
as a species that expanded its future range to more than
125% of their current predicted range, whereas contraction was classified as a species with a future range of 75%
or less of their current predicted distributions. Stable species are those with future range areas between 75 and
125% of their current predicted distributions. To examine
whether range shifts are predicted to occur, we calculated
and connected the geographic centroids of the current
and future binary SDMs in ArcGIS 10.1 using SDMtoolbox v1.0 (Brown 2014).
At the species level, expansion, stability, and contraction were also classified as a change in future range size
of: >125, 125–75, and 75–0%, respectively. Core range
shifts were calculated by measuring and connecting the
geographic centroids of the current and future binary
ª 2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
Lemurs in the Face of Climate Change
SDMs. To characterize areas of conservation priority, we
measured the following: (1) the density of overlapping
core range shift vectors to identify areas that are key to
future dispersal and (2) areas of high species richness and
high levels of micro-endemism for current and future
(2080) periods. To calculate species richness, we summed
the binary SDMs. To calculate areas of high micro-endemism, we calculated the weighted endemism, which is the
sum of the reciprocal of the total number of cells each
species in a grid cell is found within (Crisp et al. 2001).
A weighted endemism emphasizes areas that have a high
proportion of species with restricted ranges. We summed
the weighted endemism values in a 10 9 10 neighborhood (ca. 10 km2) across Madagascar to extend the comparisons of regions with high levels of micro-endemism.
Each of these metrics was standardized from 0 to 1, with
values of 1 representing the highest values of biodiversity
metrics for both time periods (current and future). Layers
were then summed, output GIS calculation depicts areas
of high species richness and micro-endemism through
time (referred to as the HSRME layer).
Lastly, because distributions were predicted to change
for many lemur species, we estimated areas of high dispersal importance for the species included here. Using the
vectors of centroid changes, we calculated the density of
overlapping vectors. This was performed in ArcGIS 10.1
using the line density function with a search radius of
50 km (ESRI 2012). Resulting areas of high line densities
reflected areas that are hypothesized to be essential for
future dispersal into suitable habitats. On the basis of these
areas, we calculated least-cost corridors (categories of paths
that include paths with slightly less suitable habitats relative
to the optimum path) between currently protected areas to
identify additional areas in need of protection. As a friction
layer (the layer used to depict the site-by-site connectivity
cost in least-cost corridor analyses), we inverted the
HSRME layer and standardized it from 0 to 1, placing a
friction value of 10 on deforested areas. Least-cost corridors
were estimated in ArcGIS 10.1 using SDMtoolbox v1.0
using the default settings (Brown 2014).
Results
We created high-performing species distribution models
for 57 species of lemurs, which were then used to generate ensemble models of each species. Model performance
was based on true skill statistic (TSS) score of ≥0.85.
True skill statistic scores range from 1 to 1, with 0
indicating predictive no ability and 1 depicting a perfect
ability to distinguish actual suitable and unsuitable habitat. Our results reveal that the majority of species (35
spp., 59.6%) are predicted to experience range contractions
in future climates. Of these species, range sizes are
1135
Lemurs in the Face of Climate Change
J. L. Brown & A. D. Yoder
(A)
(B)
(C)
(D)
(G)
1136
(E)
(F)
Figure 1. Overview of species distribution
modeling here employed. (A) Species
occurrence data and climate data were
prepared for Madagascar, (B) SDMs were built
based on ten widely used modeling techniques
(only six are pictured). (C) The resulting
models, including replicates of each method,
are filtered based on their abilities to predict
known occurrences and pseudo-absences using
a true skill statistic (TSS). (D) The resulting
models with TTS values ≥ 0.85 were projected
throughout the climate of the current
landscape and two future climate models.
(E) Models for each scenario are compiled and
compared, creating an ensemble probabilistic
density landscape depicting the probability of a
species occurrence throughout the landscape.
(F) The ensemble models were then converted
to a binary “presence/absence” landscape
from which all geospatial analyses were
performed on. (G) To create high-quality
species distribution models, we carefully
selected pseudo-absences (PAs). Here, we
select PAs within a variable distance from each
known occurrence point determined by
calculating the area of a minimum convex
polygon from occurrence points and
transforming that to reflect a logistic curve
with values from 50 to 300 km. This method is
sensitive to changes in smaller ranges, making
it suitable for use on micro-endemic to broadly
distributed species.
ª 2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
J. L. Brown & A. D. Yoder
(A)
Lemurs in the Face of Climate Change
(B)
(C)
(D)
Figure 2. Major distribution patterns predicted for lemurs resulting from future climate change. (A) Depicts areas where three or more species
will experience contraction, expansion, or stability. Cool colors correspond to areas of contraction and warmer colors depict areas of stability and
expansion. (B) Depicts areas of high species richness and/or high levels of micro-endemism for both current and future (2080) scenarios. Warm
colors depict areas with high biodiversity values at both time periods and should thus have the greatest conservation potential. (C) Map of core
range shifts depicts the predicted distribution changes (based on the centers of their distributions) of each focal species. Each line depicts
predicted distributional shifts of the species range centroid from current (start of arrow) to 2080 (end of arrow) scenarios. (D) Areas of highest
conservation concern. The line densities depicted by warmer colors illustrate areas of high overlap in core range shifts through time. In these
areas, it will be vital that protected areas are connected to facilitate dispersal into appropriate habitats.
predicted to decrease by an average of 67.1%. Of the 57
species modeled, 27 species (47%) have future distributions <50% of their current sizes; 14 species (25%) have
distributions <20% of their current size; and six species
(11%) are predicted to have distributions <1% of their
current sizes. This includes three species predicted to go
extinct (Lepilemur microdon, Lepilemur hubbardorum,
and Microcebus danfossi, Table S1). Nine lemur species
(16%) are predicted to expand their ranges in the future
(Table S1). Of those species, on average, their ranges are
predicted to expand by 180.2%. Only 13 species’
(22.8%) distribution sizes were predicted to be stable
through time with an average range area of 99.6% of
current sizes (Table 1).
Our study identified three regions of Madagascar as the
highest priorities for protection and conservation consequent to predictions of climate change. First, our analyses of
core range shifts predict a large number of distributional
shifts from east-central rainforests northward into the Masoloa peninsula (Fig. 2C,D). These shifts coincide with a
large number of lemur species predicted to experience range
contractions in the east-central rainforests and the increased
level of stability predicted in the Masoloa peninsula
(Fig. 2A). Using least-cost corridors and our estimates of
high current and future species richness and micro-endemism, we identified a series of corridors that connect these
regions, traversing the habitats of highest suitability and ecological stability for the largest number of species (Fig. 3A).
ª 2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
1137
Lemurs in the Face of Climate Change
J. L. Brown & A. D. Yoder
(A)
(C)
(C)
(A)
(D)
(B)
(B)
(E)
Figure 3. Conservation priority areas. Three regions of Madagascar with highest priority for protection and conservation of lemurs and their
cohabitants. (A) Area 1 of highest conservation concern. Core range shift analyses suggest that many of the east-central populations may need to
disperse northward via the forests between Zahamena NP, Ambatovaky, and Makira. Using least-cost corridors and estimates of high current and
future species richness and micro-endemism, we identify a series of corridors that connect these regions – traversing the habitat of the highest
suitability and ecological stability for the highest number of species. (B) Second priority area surrounding the Mangoky River. This area is predicted
to be central in facilitating dispersal among southwestern ecotones, but also a region predicted to act as a sanctuary for many lemur species. (C)
Third priority region identified is a wide range habitat in the NW. This region has experienced widespread deforestation and a stepping-stone
reserve network combined with selective reforestation might be feasible. Two specific areas with high levels of micro-endemism in this region are
the coastal forests near Ambaliha and Antsirabe (situated west of Manongarivo) and the coastal forests around the village of Mariarano (west of
Bongolava). See Fig. S2 for information regarding D and E.
The second priority region is the area surrounding the
Mangoky River in the southwest Madagascar (Fig. 3B). This
area is not only predicted to be central in facilitating dispersal for many species among southwestern ecotones, but is
also predicted to act as a sanctuary for many lemur species.
This area is expected to experience nonanalogous climates
by the year 2080, although even in light of this departure
from observed climates in Madagascar, important environmental parameters necessary to sustain species are predicted
to persist. The third priority region identified comprises a
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ª 2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
J. L. Brown & A. D. Yoder
wide range of habitats in the northwest (Fig. 3C), a region
that has experienced widespread deforestation and habitat
alteration. Consequently, a continuous NS corridor is not
currently feasible, although a stepping-stone reserve network
combined with selective reforestation might offer a solution.
The forests immediately west of Manongarivo (Fig. 3C) and
the coastal forests around the village of Mariarano (west of
Bongolava in Fig. 3C) are predicted to possess high levels of
micro-endemism and should be immediately assessed and
considered for protection.
Discussion
Ensemble species distribution models account for intermodel variability by fitting a group of species’ distribution models (SDMs) across more than one set of initial
conditions: model classes, model parameters, and boundary conditions (see Araujo and New 2007 for a review).
The use of a large ensemble from many SDMs, the uncertainties in the area bounded by the models, predictive
consensus, and the probabilistic likelihood of a species
occurrence are minimized (Araujo and New 2007; Thuiller 2007); and therefore, the final model is a conservative
agreement of input parameter space and modeling algorithms. Here, in addition to the more commonly used
temperature and precipitation climate data, we incorporated climate data on soil moisture and solar radiation
data into our models. The resulting predictions therefore
integrate the most detailed climatic, ecological, and biological data applied so far toward understanding the current status of lemur distributions and their probable
fluctuations in response to climate change. These data are
critical for making informed decisions pertaining to habitat protection with the goal of conserving lemurs and
other members of Madagascar’s irreplaceable biota.
The IUCN Red List shows that 94% of lemur species
are currently threatened. Our study predicts that 60% of
the 57 species modeled by in this analysis will experience
considerable range reductions in the next 70 years entirely
due to future climate change. Thus, the conservation
impacts of these predictions should be considered highly
conservative in that they ignore the progressive and ongoing effects of human-mediated habitat destruction. In
context of spatial patterns, our estimates predict a large
area of range contraction, >600 km in length, for up to
12 species of lemurs in the mid-elevation eastern rainforests. Other areas also are predicted to experience a large
reduction in lemur species’ ranges, for example, around
Ankarafantsika in the northwest (Fig. 2A). Although our
predictions are more extreme than estimates of range
reductions of 11–27% that are based solely on models of
vegetative change (Malcolm et al. 2006), they are nonetheless conservative in that they do not include many
ª 2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
Lemurs in the Face of Climate Change
micro-endemic species due to limited locality data available for modeling. Given that species with small distributions typically possess narrower ecological tolerances, and
even slight environmental changes can dramatically affect
those species (Murray et al. 2011), future consideration of
micro-endemics may reveal even greater threats.
Our predictions and recommendations are based on
hypothesized trends of species’ range shifts with uncertainties minimized by the use of the ensemble modeling framework. Other factors impact species and their habitats (i.e.,
sociopolitical factors, biotic interactions among native species, or novel invasive species and diseases; e.g., see Chapman et al. 2014), but these are not explicitly included in
our models. For example, climate change will also affect
human populations in Madagascar – this includes changes
in agricultural practices or tracking suitable agricultural
sites. Even in light of these volatile extrinsic factors, many
studies have demonstrated that climate-based model predictions can be highly accurate for anticipating range-shifting species (e.g., Morin and Thuiller 2009; Anderson
2013).
The most significant result to emerge from this study
is the prediction that a majority of lemur species will
experience range shifts – mostly contractions, in some
cases by hundreds of kilometers. In many affected areas,
suitable habitat does not exist between current and future
distributions to allow for lemur species to traverse the
landscape in pursuit of appropriate environments. To
prepare for these contingencies, we urge that the conservation and local Malagasy governing communities place a
priority on the establishment and maintenance of targeted forest corridors that will allow for these inevitable
changes in lemur distributional ranges (Moilanen et al.
2009).
Acknowledgments
We acknowledge the many contributors and editors of
Lemur News and from REBIOMA, from which a large
sample of locality points were ascertained. JLB received
salary support from Duke University Start-up funds to
ADY. This is Duke Lemur Center publication number
1279. We are grateful to Robert P. Anderson, two anonymous reviewers, and to the members of the Yoder lab for
many insightful comments on this manuscript.
Conflict of Interest
None declared.
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Supporting Information
Additional Supporting Information may be found in the
online version of this article:
Figure S1. Spatial patterns of distributional changes.
Warm colors depict higher values of each category; gray
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J. L. Brown & A. D. Yoder
values depict human altered vegetation. A. Contraction,
B. Stability, and C. Expansion of species distributions.
Figure S2. Conservation priorities (continued from Figure
2). E. A proposed corridor in southeastern Madagascar
connecting Andohahala NP to Midongy Betotaka NP and
then to Fandrina Vondrozo. D. A proposed corridor in
east-central Madagascar connecting Ranomafana NP to
Fadrina Vondrozo to Zahemena-Ankeniheny.
Figure S3. Shared areas of nonanalogous climate space in
the two future climate scenarios. Here, more intense red
indicates higher divergences from observed climate space
and are climates that do not exist anywhere in contemporary Madagascar. The map, a MESS analysis, was generated in MaxEnt. The scatter plot depicts the first two PCs
from single PCA analyses containing all three climate sce-
narios throughout Madagascar. The dotted areas are the
major nonanalogue areas depicted in the map.
Figure S4. Lepilemur hubbardorum from ZombitseVohibasia National Park is one of three species predicted
to be driven to extinction entirely due to future climate
change, reports Brown & Yoder (2015). Photo: Jason L.
Brown.
Table S1. Start region in core range shifts depicts area
based on the subdivision of Madagascar into four units
based on a grid as follows: NW=1, NE=2, SE=3, and
SW=4. ICUN Red list status: CR = critically endangered;
EN = endangered; VU = vulnerable; NT = near threatened; LC = least concern; and DD = data deficient.
Table S2. Table of occurrence localities used for this
study and their sources.
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ª 2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.